FDA Approves First Clinical Cloud-Based Deep Learning In Healthcare

It is a big stride forward for AI and machine learning in healthcare, as FDA approves a new machine learning application for medical imaging.

The medical imaging platform developed by Arterys has been approved for use in aiding doctors diagnose heart problems. The platform is a cloud-based self-teaching neural network that has learned from 1000 cases thus far, and it will continue to learn and improve its knowledge as it examines more number of cases.

Unlike traditional medical imaging software, Arterys Cardio DLTM uses deep learning, a form of artificial intelligence, to automate time-consuming analyses and tasks that are performed manually by clinicians today. The physician can edit the automated contours if desired. These images show the Cardio DLTM generated contours of the insides and outsides of the ventricles of the heart. The software can process a scan in just 10 seconds, compared to manual contouring performed by clinicians. Source: PRNewswire

The FDA approval was granted based on the platform’s ability to deliver results as accurately as qualified professionals in the field. However, the key difference lies in the time required for analysis. Arterys takes only 15 seconds to produce the results for one case, which usually requires 30 minutes to an hour if analyzed by a professional.

The founders of Arterys – Fabien Beckers, John Axerio-Cilies, Albert Hsiao and Shreyas Vasanawala put together the platform to help physicians in their diagnosis and to understand the functioning of the heart.

“This is a huge deal – it’s the first time this new way of imaging has been cleared for clinical application. It’s about truly helping clinical workflow to move into the cloud and deep learning and do something pretty substantial. It opens the seals, and sets a precedent for what can be done” Becker explained.

The platform dubbed as Arterys Cardio DL came up with 10 million rules based on connections it found within initial data obtained from 1000 cases. Based on this data it helps diagnose heart problems from new data with minimal or no manual intervention.

“We’re trying to make it quantitative and data-driven. We started with the heart because that is one of the hardest organs to do – and now we know we can do this, we can use it in many other areas” said Becker.

“With the heart, the left ventricle is kind of rounded but very straightforward, whereas the right is peanut-shaped and kind of complex. Proving that this technology can be used to analyze images of both is a big deal, because it’s something that’s taking too long using the conventional approach. “Doing this really shows how dramatically and profoundly these technologies can be helpful.”

Since the Arterys’s platform is cloud-based, it enables physicians to come together with data from all around the world, and in turn, the platform will continue to teach itself from these data which will improve its accuracy.

However, this cloud-based analysis poses certain problems in terms of patient privacy. This was a major cause of concern to use such platform in healthcare due to fear of data piracy.

Arterys came up with a solution to counteract this by a system known as PHI Service which enables the personal identifying information to be stripped from the data at the point where it is collected- usually a hospital. When an accredited user logs in, the system grabs the imaging data and results from Arterys’s cloud and the secure PHI data from the hospital server and rebuilds it. More importantly, Arterys itself never receives any information which can be used to identify individuals.

The clearance of such an AI-based system by FDA is a huge step forward for data and image analytics in healthcare. It will undoubtedly open doors for many such machine learning platforms that would greatly help physicians diagnose and treat disease early. Arterys itself is well on the way to produce the application of its technology for other diseases such as cancer.

Arundithi holds a bachelor's degree in Biotechnology from SASTRA University, Tanjore, India. She is currently a PhD student in Nanyang Technological University. She works on the synthesis of graphene quantum dots for biomedical applications.